12.1:
What is an Experiment?
An experiment is a systematic approach to prove or disprove a hypothesis and uncover new knowledge. Statistics is often used to interpret the results of an experiment.
Consider an experiment to determine the relationship between mobile phone usage and sleep quality among college students.
These students, referred to as subjects, are divided into two groups. One is experimental, while the other is a control group. Only the experimental group is allowed to use mobile phones thirty minutes before bedtime, while the control group acts as the standard of comparison.
Researchers then record the number of hours of sleep and sleep latency or variables – the characteristics of subjects that are examined, measured, and interpreted.
Variables are of two types: dependent and independent.
Mobile phone usage is an independent variable controlled by researchers, and sleep hours and sleep latency are dependent variables that are measured and recorded.
Finally, hypothesis testing is used to infer whether the observed changes in sleep quality in the experimental group over the control group are statistically significant.
12.1:
What is an Experiment?
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The different values of the explanatory variable are called treatments. An experimental unit is a single object or individual to be measured.
You want to investigate the effectiveness of vitamin E in preventing disease. You recruit a group of subjects and ask them if they regularly take vitamin E. You notice that the subjects who take vitamin E exhibit better health on average than those who do not. Does this prove that vitamin E is effective in disease prevention? It does not. There are many differences between the two groups compared in addition to vitamin E consumption. People who take vitamin E regularly often take other steps to improve their health: exercise, diet, other vitamin supplements, and choosing not to smoke. Any one of these factors could be influencing health. As described, this study does not prove that vitamin E is the key to disease prevention.
Additional variables that can cloud a study are called lurking variables. In order to prove that the explanatory variable is causing a change in the response variable, it is necessary to isolate the explanatory variable. The researcher must design her experiment in such a way that there is only one difference between the groups being compared: the planned treatments. This is accomplished by the random assignment of experimental units to treatment groups. When subjects are assigned treatments randomly, all of the potential lurking variables are spread equally among the groups. At this point, the only difference between groups is the one imposed by the researcher. Therefore, different outcomes measured in the response variable must be a direct result of the different treatments. In this way, an experiment can prove a cause-and-effect connection between the explanatory and response variables.
This text is adapted from Openstax, Introductory Statistics, Section 1.4 Experimental Design and Ethics